import tensorflow as tf

x = tf.random.normal([2, 4])
w = tf.random.normal([4, 3])
b = tf.zeros([3])
y = tf.constant([2, 0])

with tf.GradientTape() as tape:
    tape.watch([w, b])
    # 直接使用logits, 避免softmax不稳定问题
    logits = x @ w + b
    loss = tf.reduce_mean(tf.losses.categorical_crossentropy(tf.one_hot(y, depth=3), logits, from_logits=True))
gradient = tape.gradient(loss, [w, b])
print(gradient)
